machine-learning-notebook-series

This tutorial is machine learning and deep learning domain, this contains whole steps from Feature Engineering, Model Building, Model Selection, Model Evalution for machine learnig that should be used with step by step comments in these notebooks. If you need to learn math basic for machine learing, you could also take a look at Linear Algebra, if you want to plot some graphs during your training step, you could also glimpse Matplotlib and Seaborn tutorials, if you need to learn something about popular frameworks like TensorFlow and PyTorch in deep learning, you could also get these tutorials bellow. There are also some data structures with pure python implement, you could also take a reference!

Dependencies

• Python 3.6
  • numpy 1.17.0
  • pandas 0.25.0
  • matplotlib 2.2.4
  • seaborn 0.9.0
  • sklearn 0.21.3
  • PyTorch 1.2.0
  • TensorFlow 1.14.0
  • Keras 2.1.2

Tutorial Lists(You could just dive into the parts that interest you)

Machine Learning Tutorials

Linear Algebra:

• Linear Algebra Tutorial

Machine Learning Pipeline:

• Preprocessing Tutorial
• Natural Language Preprocessing Tutorial
• Common Algorithms
• Model Parameters Tuning
• Model Selection Tutorial
• Whole Model Building Pipeline

Machine learning algorithms

• Adaboost Implement

Deep Learning Tutorials

• PyTorch Starts Here Tutorial

  • PyTorch Linear Regression Tutorial
  • PyTorch Logistic Regression Tutorial
  • PyTorch Feed Forward Network Tutorial
  • PyTorch Convolutional network Tutorial
  • PyTorch LSTM Tutorial
  • PyTorch Bidirectional LSTM Tutorial

• TensorFlow Starts Here Tutorial

  • Keras tutorial

Plot Tutorials:

• Matplotlib Tutorial
• Seaborn Tutorial

Data Structures:

• Data structures jupyter
• Queue
• Stack
• Linked List
• Doubly Linked List
• Binary Search Tree

Other Materials

• Git Tutorial
• SQL Server Tutorial

Additional Materials:

Here you could get something useful for machine learning basic! I really recommend you should reference these materials for theory!

• Machine Learning Cheatsheet
• Probability Cheatsheet
• How to read paper
• Review of Linear Algebra
• Review of Probability

I will put more and more tutorials in this series steadily!

I'm really glad to hear your voice for these tutorials, if you learn something from this repository, I will be happier!

TODO LIST:

Algorithms:

preprocessing for image tutorials | Basic DNN tutorials | CNN tutorials | RNN\LSTM\GRU tutorials | Attension\Transformer tutorials

Modules:

NLTK tutorials | Gensim tutorials | Keras tutorials | TensorFlow tutorials | MXNet tutorials | PySpark tutorials |